CUPiDRT: Detecting Improper GPU Usage in Real-Time Applications

Computer-vision applications typically rely on graphics processing units (GPUs) to accelerate computations. However, prior work has shown that care must be taken when using GPUs in real-time systems subject to strict timing constraints; without such care, GPU use can easily lead to unexpected delays not only on the GPU device but also on the host CPU. In this paper, a software library is presented that can detect the improper use of GPUs for safety-critical computer-vision applications. This library was used to analyze several GPU-using sample applications available as part of OpenCV, a popular computer-vision library, revealing the presence of issues in all ten applications considered. Additionally, a case study is presented, detailing the response-time improvements to one of the applications when such issues are corrected.